Kuroki Manabu, Cai Zhihong
Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, Japan.
Stat Med. 2008 Dec 30;27(30):6597-611. doi: 10.1002/sim.3430.
This paper considers the problem of evaluating the causal effect of an exposure on an outcome in observational studies with both measured and unmeasured confounders between the exposure and the outcome. Under such a situation, MacLehose et al. (Epidemiology 2005; 16:548-555) applied linear programming optimization software to find the minimum and maximum possible values of the causal effect for specific numerical data. In this paper, we apply the symbolic Balke-Pearl linear programming method (Probabilistic counterfactuals: semantics, computation, and applications. Ph.D. Thesis, UCLA Cognitive Systems Laboratory, 1995; J. Amer. Statist. Assoc. 1997; 92:1172-1176) to derive the simple closed-form expressions for the lower and upper bounds on causal effects under various assumptions of monotonicity. These universal bounds enable epidemiologists and medical researchers to assess causal effects from observed data with minimum computational effort, and they further shed light on the accuracy of the assessment.
本文探讨了在观察性研究中评估暴露因素对结局的因果效应这一问题，其中暴露因素与结局之间存在已测量和未测量的混杂因素。在这种情况下，MacLehose等人（《流行病学》2005年；16:548 - 555）应用线性规划优化软件来确定特定数值数据下因果效应的最小和最大可能值。在本文中，我们应用符号Balke - Pearl线性规划方法（《概率性反事实：语义、计算及应用》。博士论文，加州大学洛杉矶分校认知系统实验室，1995年；《美国统计协会杂志》1997年；92:1172 - 1176）来推导在各种单调性假设下因果效应上下界的简单封闭形式表达式。这些通用界使流行病学家和医学研究人员能够以最小的计算量从观察数据中评估因果效应，并且进一步阐明了评估的准确性。
